{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Week 9. Final project: COVID-19 and government response " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I decided to conduct my Week 9 research project on exploring COVID statistics and its correlation with new indicators of government response or measures taken against COVID virus spreading." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Research questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- is there a significant correlation between cumulative deaths from COVID-19 and maximum government response index?\n", "\n", "- is there a significant correlation between government response index and government funding healthcare in previous years?\n", "\n", "- is there strong positive correlation between confirmed COVID-19 cases and government response index over time?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hypothesis to check" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- yes, there is significant positive correlation between deaths from COVID-19 and maximum government response;\n", "\n", "- no, strict response to COVID-19 in 2020 and better funding healthcare systems in 2019 are not significantly correlated.\n", "\n", "- yes, correlation between confirmed cases and government response over time is strong." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To explore COVID statistics I use COVID-19 Data Repository by the Center for Systems Science and Engineering at Johns Hopkins University, or JHU CSSE COVID-19 Dataset (here - Dataset 1) accessing it via API.\n", "\n", "To explore new indicators of government response (measures taken against COVID virus spreading) I use Oxford Covid-19 Government Response Tracker (here - Dataset 2)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That is why Part I of this project presented as chapter \"Dataset 1: Johns Hopkins University & Medicine (JHU)\" and its paragraphs, and Part II is presented as \"Dataset 2: Oxford Covid-19 Government Response Tracker\"." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dataset 1: Johns Hopkins University & Medicine (JHU)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Timeseries from January 22, 2020 to August 20, 2020 are available for downloading:\n", "https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Raw data links to cumulative data:\n", "\n", "Confirmed cases: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\n", "\n", "Deaths: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\n", "\n", "Recovered: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Confirmed cases statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §1.1. Cleaning and preparing for animation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The GIS technologies have played an important role in many aspects, including the data integration, and geospatial visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, and many more. These provide support information for government sectors to fight against the COVID-19 spreading. \n", "\n", "The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University & Medicine (JHU) had provided the dashboard created with ESRI ArcGIS operation dashboard (https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6). But feature for visualizing the change of data overtime on the map is missing. Later JHU created animated map on confirmed cases here https://coronavirus.jhu.edu/data/animated-world-map , separately from the dashboard, but the users can only observe the map changing colors, they have no access to view the actual numbers or zoom in the map, as it is not interactive and does not show the actual data.\n", "\n", "So I decided to create animated maps to explore data changes over time. In order to do that my current dataset structure should be changed. Now the data structure is that every day's statistics is a separate column, so the values are \"scattered\" in unique cells for each day and country; I will move all the values to a single \"value\" column, and move all days labels from columns names to single \"Date\" column. It will transform the dataset to its long variation with repeating country rows and date rows." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd #to work with tabular data\n", "import pycountry #to get the three-letter country codes ISO 3166–1 for each country\n", "\n", "df_cases=pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\")\n", "\n", "# Aggregate the dataset\n", "df_cases = df_cases.drop(columns=['Province/State','Lat','Long'])\n", "df_cases = df_cases.groupby('Country/Region').agg('sum')\n", "date_list = list(df_cases.columns)\n", "\n", "# Get the country codes for each country\n", "#list(pycountry.countries) #uncomment to load the list of available data\n", "\n", "def get_country_code(name):\n", " \"\"\"\n", " Return ISO-3 letter code for country by its name; \n", " Return None if name is not found in the pycountry.countries\n", " \"\"\"\n", " try:\n", " return pycountry.countries.lookup(name).alpha_3\n", " except:\n", " return None\n", "\n", "df_cases['Country'] = df_cases.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several countries' names are written differently than pycountry expects, so I change their names to match and get the code" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df_cases.loc[df_cases.Country==\"Burma\",'Country']='Myanmar'\n", "df_cases.loc[df_cases.Country==\"Brunei\",'Country']='Brunei Darussalam'\n", "df_cases.loc[df_cases.Country==\"Iran\",'Country']='Iran, Islamic Republic of'\n", "df_cases.loc[df_cases.Country==\"Congo (Brazzaville)\",'Country']='Congo, The Democratic Republic of the'\n", "df_cases.loc[df_cases.Country==\"Congo (Kinshasa)\",'Country']='Republic of the Congo'\n", "df_cases.loc[df_cases.Country==\"Cote d'Ivoire\",'Country']=\"Côte d'Ivoire\"\n", "df_cases.loc[df_cases.Country==\"Korea, South\",'Country']=\"Korea, Republic of\"\n", "df_cases.loc[df_cases.Country==\"Syria\",'Country']=\"Syrian Arab Republic\"\n", "df_cases.loc[df_cases.Country==\"Taiwan*\",'Country']=\"Taiwan, Province of China\"\n", "df_cases.loc[df_cases.Country==\"Russia\",'Country']='Russian Federation'\n", "df_cases.loc[df_cases.Country==\"West Bank and Gaza\",'Country']='Palestine, State of'\n", "df_cases.loc[df_cases.Country==\"Venezuela\",'Country']='Venezuela, Bolivarian Republic of'\n", "df_cases.loc[df_cases.Country==\"US\",'Country']='United States'\n", "df_cases.loc[df_cases.Country==\"Laos\",'Country']=\"Lao People's Democratic Republic\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As soon as names are unified, I can add their ISO-3 codes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "188\n" ] } ], "source": [ "print(len(df_cases['Country'].tolist()))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df_cases['ISO-3'] = df_cases['Country'].apply(get_country_code)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Transform the dataset in a long format\n", "df_cases = pd.melt(df_cases, id_vars=['Country','ISO-3'], value_vars=date_list)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Rename columns\n", "df_cases = df_cases.rename(columns={\"variable\": \"Date\",\"value\": \"Value\"})" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [], "source": [ "#df_cases[0:60] #checking the slice 1 in the dataset on confirmed cases, uncomment to load" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is one \"None\" value left in the df_confirmed_long[0:60] slice (\"Diamond Princess\")." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "#df_cases[60:120] #checking the slice 2 in the dataset on confirmed cases, uncomment to load" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#df_cases[120:160] #checking the slice 3 in the dataset on confirmed cases, uncomment to load\n", "#df_cases[160:188] #checking the slice 4 in the dataset on confirmed cases, uncomment to load" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are \"None\" values for \"MS Zaandam\", \"Holy See\" (Vatican) and \"Kosovo\" left in the df_confirmed_long[60:120] slice. First is not a country, second is too small and excessive to dataset (population is 809 people), but Kosovo is important to show on the map as this European country has population more than 1.8 mln people and 11 thousands of confirmed cases.\n", "\n", "The problem is, that \"Kosovo\" is not listed in pycountry dictionary (although the World Bank added XKX code to Kosovo in June 2017 according to archives https://libraries.acm.org/binaries/content/assets/libraries/archive/world-bank-list-of-economies.pdf), that is why I need to \"fix Kosovo\" after adding all other codes with apply(get_country_code). " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#add ISO-3 code manually as it is not listed in pycountry dictionary\n", "df_cases.loc[df_cases.Country==\"Kosovo\",'ISO-3']=\"XKX\"" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Country ISO-3 Date Value\n", "92 Kosovo XKX 1/22/20 0\n", "280 Kosovo XKX 1/23/20 0\n", "468 Kosovo XKX 1/24/20 0\n", "656 Kosovo XKX 1/25/20 0\n", "844 Kosovo XKX 1/26/20 0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check ISO-3 for \"Kosovo\" to make sure the code is applied\n", "df_cases[df_cases['ISO-3']=='XKX'][:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it is safe to drop \"None\" values." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Country ISO-3 Date Value\n", "47 Denmark DNK 1/22/20 0\n", "49 Djibouti DJI 1/22/20 0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cases = df_cases.dropna()\n", "df_cases[47:49] #there is no Diamond Princess in the dataset anymore, its index was 48" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Country ISO-3 Date Value\n", "103 Luxembourg LUX 1/22/20 0\n", "105 Madagascar MDG 1/22/20 0\n", "106 Malawi MWI 1/22/20 0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cases[101:104] #there is no MS Zaandam in the dataset anymore, its index was 104" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dataset is cleaned and has data on 185 countries over January - August 2020." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "185\n", "39590\n", "True\n" ] } ], "source": [ "print(len(df_cases['ISO-3'].unique().tolist()))\n", "print(len(df_cases['ISO-3']))\n", "print((len(df_cases['ISO-3']))==(len(df_cases['Date']))==(len(df_cases['Value'])))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Country False\n", "ISO-3 False\n", "Date False\n", "Value False\n", "dtype: bool" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cases.isnull().any() # check for NaN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §1.2. Animation of the map over time: cases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I can use Plotly Express to create animated map. The cumulative cases animation shows the total number of cases reported in each country at each point in time, regardless of how many people have recovered. Visualizing cumulative cases demonstrates the overall toll of coronavirus on a country over time." ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "customdata": [ [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 548 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 2 ], [ 0 ], [ 0 ], [ 0 ], [ 1 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 1 ], [ 0 ], [ 0 ], [ 2 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 1 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ] ], "geo": "geo", "hovertemplate": "%{hovertext}

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January 22, 2020 - August 20, 2020" }, "updatemenus": [ { "buttons": [ { "args": [ null, { "frame": { "duration": 500, "redraw": true }, "fromcurrent": true, "mode": "immediate", "transition": { "duration": 500, "easing": "linear" } } ], "label": "▶", "method": "animate" }, { "args": [ [ null ], { "frame": { "duration": 0, "redraw": true }, "fromcurrent": true, "mode": "immediate", "transition": { "duration": 0, "easing": "linear" } } ], "label": "◼", "method": "animate" } ], "direction": "left", "pad": { "r": 10, "t": 70 }, "showactive": false, "type": "buttons", "x": 0.1, "xanchor": "right", "y": 0, "yanchor": "top" } ] } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.express as px\n", "import numpy as np\n", "df = df_cases\n", "fig = px.choropleth(df, # input dataframe\n", " locationmode='ISO-3', # set of locations used to map 'locations' \n", " locations=\"ISO-3\", # identify country by code\n", " color=np.log10(df['Value']), # identify values and replace linear scale with logarithmic scale\n", " hover_name=\"Country\", # identify column to add as name to hover information\n", " animation_frame=\"Date\", # identify date column\n", " projection=\"equirectangular\", # select projection\n", " hover_data=[df['Value']], # hover text\n", " center = {\"lat\": 14.883333, \"lon\": 5.266667},# set map center\n", " color_continuous_scale=px.colors.sequential.Reds, # set color scale, \"_r\" to reverse color\n", " range_color=[0,round(np.log10(df['Value']).max(),2)], # set the range of dataset\n", " )\n", "\n", "#customize layout\n", "fig.update_layout(\n", " title_text='Confirmed cases by country over time
January 22, 2020 - August 20, 2020',\n", " geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'),\n", " \n", " annotations = [dict(\n", " x=0.8,\n", " y=0.0,\n", " xref='paper',\n", " yref='paper',\n", " text='Source: \\\n", " Johns Hopkins University & Medicine',\n", " showarrow = False\n", " )],\n", " \n", " #customize colorbar\n", " coloraxis_colorbar=dict(\n", " title='Confirmed',\n", " tickvals=[0, 1, 2, 3, 4, 5, 6, 6.7], #customize colorbar title and ticks values\n", " ticktext = ['1', '10','100', '1K', '10K', '100K', '1M', '6M'] #replace log10 colorbar ticks text\n", " )\n", " \n", " )\n", "fig.show() \n", "fig.write_html(\"Confirmed_map.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The map has \"Play\" and \"Stop\" buttons near the Date of observation mark, and allows zooming and observing the number of cases, ISO-3 codes and dates of observation in hover info for each country. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The map reveals later some interesting details. JHU CSSE COVID-19 Dataset does not contain information on confirmed cases in Somaliland (part of Somalia, ISO-3 code of Somalia is \"SOM\"), North Korea (ISO-3 code is \"PRK\") and Turkmenistan (\"TKM\"). \n", "\n", "Somaliland has declared independence, but is not recognized internationally (hence not in the ISO list), so choropleth module has to particular code to use to map the data. Plotting by country name is also not possible because Somaliland borders are not interationally set and recognized. \n", "\n", "North Korea escalates coronavirus response, but extent of outbreak is unclear; there are no confirmed cases of COVID-19 in North Korea, the government has taken extensive measures, including quarantines and travel restrictions. North Korea didn't admit to its 1st case until July, although city of Kaesong has been focus of quarantines. Since the end of December till August, according to unofficial data North Korea has quarantined and released 25,905 people, 382 of them foreigners.\n", "\n", "Lack of information is not surprising in the first and the second case, but Turkmenistan is missing for different reasons. There is no official statistics on COVID-19 spread in Turkmenistan at all. The state-controlled media are not allowed to use the word \"coronavirus\" and it has even been removed from health information brochures distributed in schools, hospitals and workplaces (according to Turkmenistan Chronicle, one of the few sources of independent news, whose site is blocked within the country). Turkmenistan 2020 population is estimated at 6.0 mln people at mid year according to UN data." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max confirmed cases: 5667112\n" ] } ], "source": [ "print(\"Max confirmed cases:\", df_cases['Value'].max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As of August 20, 2020 maximum number of cases - 5.6 mln - were confirmed in USA, and there were performed about 69.6 mln tests there. Testing has covered every 208 out of 1000 people in the country." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §1.3. Structure by region and income level: cases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I would like to see bigger picture for data, not only by country, but also by region and by income level. To make this happen I add region and income level colunms to all countries. I use the World Bank data to create dataframe-converter and merge additional columns to my dataset. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ISO-3RegionIncomeLevelCountry_WB
0AFGSouth AsiaLow incomeAfghanistan
1AGOSub-Saharan AfricaLower middle incomeAngola
2ALBEurope & Central AsiaUpper middle incomeAlbania
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" ], "text/plain": [ " ISO-3 Region IncomeLevel Country_WB\n", "0 AFG South Asia Low income Afghanistan\n", "1 AGO Sub-Saharan Africa Lower middle income Angola\n", "2 ALB Europe & Central Asia Upper middle income Albania" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_convert=pd.read_csv(\"iso3_region_income_country.csv\")\n", "df_convert.head(3)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryISO-3DateValueRegionIncomeLevelCountry_WB
0AfghanistanAFG1/22/200South AsiaLow incomeAfghanistan
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" ], "text/plain": [ " Country ISO-3 Date Value Region IncomeLevel Country_WB\n", "0 Afghanistan AFG 1/22/20 0 South Asia Low income Afghanistan" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cases=df_cases.merge(df_convert,on='ISO-3')\n", "df_cases.head(1)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Country False\n", "ISO-3 False\n", "Date False\n", "Value False\n", "Region False\n", "IncomeLevel False\n", "Country_WB False\n", "dtype: bool" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cases.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interactive sunburst plot represents hierarchial data as sectors laid out over several levels of concentric rings.\n", "Next sunburst graph shows countries within world's regions where the most cases of virus were confirmed. It is United States and Brazil in Americas, India - in South Asia, Russia - in Europe and Central Asia, and South Africa in African continent." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "branchvalues": "total", "customdata": [ [ 37894 ], [ 8119 ], [ 40667 ], [ 1045 ], [ 2068 ], [ 94 ], [ 329043 ], [ 42477 ], [ 24602 ], [ 24762 ], [ 34921 ], [ 1703 ], [ 48661 ], [ 290360 ], [ 157 ], [ 70111 ], [ 80894 ], [ 648 ], [ 2095 ], [ 154 ], [ 107435 ], [ 17396 ], [ 1308 ], [ 3532330 ], [ 143 ], [ 15131 ], [ 1297 ], [ 426 ], [ 3412 ], [ 273 ], [ 18762 ], [ 126319 ], [ 4679 ], [ 981 ], [ 393769 ], [ 89616 ], [ 522138 ], [ 417 ], [ 3850 ], [ 9802 ], [ 32134 ], [ 17310 ], [ 7594 ], [ 3582 ], [ 1406 ], [ 21551 ], [ 16525 ], [ 5382 ], [ 18 ], [ 89867 ], [ 106481 ], [ 97148 ], [ 24200 ], [ 4926 ], [ 306 ], [ 2244 ], [ 4128 ], [ 37665 ], [ 28 ], [ 7871 ], [ 271960 ], [ 8388 ], [ 2437 ], [ 1385 ], [ 233029 ], [ 43325 ], [ 8138 ], [ 24 ], [ 66941 ], [ 8932 ], [ 2149 ], [ 881 ], [ 8016 ], [ 53381 ], [ 5098 ], [ 2050 ], [ 2975701 ], [ 149408 ], [ 354764 ], [ 197085 ], [ 27755 ], [ 100716 ], [ 257065 ], [ 1346 ], [ 60949 ], [ 1532 ], [ 104313 ], [ 31763 ], [ 17002 ], [ 12168 ], [ 79269 ], [ 42703 ], [ 22 ], [ 1330 ], [ 11580 ], [ 1015 ], [ 1285 ], [ 10121 ], [ 99 ], [ 2564 ], [ 7704 ], [ 14218 ], [ 5322 ], [ 9249 ], [ 6564 ], [ 2688 ], [ 1546 ], [ 6885 ], [ 346 ], [ 549734 ], [ 32484 ], [ 154 ], [ 298 ], [ 4277 ], [ 49247 ], [ 3195 ], [ 435 ], [ 5227 ], [ 30483 ], [ 67456 ], [ 1671 ], [ 4311 ], [ 1172 ], [ 51304 ], [ 13308 ], [ 10275 ], [ 83769 ], [ 291588 ], [ 84392 ], [ 361 ], [ 12536 ], [ 567059 ], [ 182365 ], [ 60281 ], [ 55211 ], [ 116481 ], [ 76355 ], [ 944671 ], [ 2780 ], [ 704 ], [ 891 ], [ 305186 ], [ 12689 ], [ 30378 ], [ 132 ], [ 1972 ], [ 56216 ], [ 3225 ], [ 2574 ], [ 3265 ], [ 603338 ], [ 2497 ], [ 386054 ], [ 2941 ], [ 17 ], [ 26 ], [ 58 ], [ 12623 ], [ 3460 ], [ 86068 ], [ 39332 ], [ 2073 ], [ 487 ], [ 8241 ], [ 509 ], [ 3390 ], [ 25 ], [ 1239 ], [ 864 ], [ 2607 ], [ 255723 ], [ 1848 ], [ 102948 ], [ 66193 ], [ 325263 ], [ 5622540 ], [ 1516 ], [ 38074 ], [ 38219 ], [ 1009 ], [ 18313 ], [ 10 ], [ 1906 ], [ 10627 ], [ 5815 ], [ 119624.8435241294 ], [ 381606.1860061191 ], [ 2093793.8020650574 ], [ 198048.0941561089 ], [ 5501772.184943308 ], [ 2482763.4573850594 ], [ 386988.96481535694 ] ], "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "hovertemplate": "labels=%{label}
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by August 21, 2020',\n", " color_continuous_midpoint=np.average(df.Value,weights=df.Value))\n", "fig.show()\n", "fig.write_html(\"Confirmed_region&country.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is there a pattern in terms of virus spread between different regions of income? The next sunburst graph shows that 'High income' countries and 'Upper medium income' countries cover 41% and 39% of total COVID-19 cases respectively; \"Lower middle income\" countries cover less than 19.5% of total COVID-19 cases, and share of cases confirmed in low income countries is about 0.5%. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "branchvalues": "total", "customdata": [ [ 37894 ], [ 8119 ], [ 40667 ], [ 1045 ], [ 2068 ], [ 94 ], [ 329043 ], [ 42477 ], [ 24602 ], [ 24762 ], [ 34921 ], [ 1703 ], [ 48661 ], [ 290360 ], [ 157 ], [ 70111 ], [ 80894 ], [ 648 ], [ 2095 ], [ 154 ], [ 107435 ], [ 17396 ], [ 1308 ], [ 3532330 ], [ 143 ], [ 15131 ], [ 1297 ], [ 426 ], [ 3412 ], [ 273 ], [ 18762 ], [ 126319 ], [ 4679 ], [ 981 ], [ 393769 ], [ 89616 ], [ 522138 ], [ 417 ], [ 3850 ], [ 9802 ], [ 32134 ], [ 17310 ], [ 7594 ], [ 3582 ], [ 1406 ], [ 21551 ], [ 16525 ], [ 5382 ], [ 18 ], [ 89867 ], [ 106481 ], [ 97148 ], [ 24200 ], [ 4926 ], [ 306 ], [ 2244 ], [ 4128 ], [ 37665 ], [ 28 ], [ 7871 ], [ 271960 ], [ 8388 ], [ 2437 ], [ 1385 ], [ 233029 ], [ 43325 ], [ 8138 ], [ 24 ], [ 66941 ], [ 8932 ], [ 2149 ], [ 881 ], [ 8016 ], [ 53381 ], [ 5098 ], [ 2050 ], [ 2975701 ], [ 149408 ], [ 354764 ], [ 197085 ], [ 27755 ], [ 100716 ], [ 257065 ], [ 1346 ], [ 60949 ], [ 1532 ], [ 104313 ], [ 31763 ], [ 17002 ], [ 12168 ], [ 79269 ], [ 42703 ], [ 22 ], [ 1330 ], [ 11580 ], [ 1015 ], [ 1285 ], [ 10121 ], [ 99 ], [ 2564 ], [ 7704 ], [ 14218 ], [ 5322 ], [ 9249 ], [ 6564 ], [ 2688 ], [ 1546 ], [ 6885 ], [ 346 ], [ 549734 ], [ 32484 ], [ 154 ], [ 298 ], [ 4277 ], [ 49247 ], [ 3195 ], [ 435 ], [ 5227 ], [ 30483 ], [ 67456 ], [ 1671 ], [ 4311 ], [ 1172 ], [ 51304 ], [ 13308 ], [ 10275 ], [ 83769 ], [ 291588 ], [ 84392 ], [ 361 ], [ 12536 ], [ 567059 ], [ 182365 ], [ 60281 ], [ 55211 ], [ 116481 ], [ 76355 ], [ 944671 ], [ 2780 ], [ 704 ], [ 891 ], [ 305186 ], [ 12689 ], [ 30378 ], [ 132 ], [ 1972 ], [ 56216 ], [ 3225 ], [ 2574 ], [ 3265 ], [ 603338 ], [ 2497 ], [ 386054 ], [ 2941 ], [ 17 ], [ 26 ], [ 58 ], [ 12623 ], [ 3460 ], [ 86068 ], [ 39332 ], [ 2073 ], [ 487 ], [ 8241 ], [ 509 ], [ 3390 ], [ 25 ], [ 1239 ], [ 864 ], [ 2607 ], [ 255723 ], [ 1848 ], [ 102948 ], [ 66193 ], [ 325263 ], [ 5622540 ], [ 1516 ], [ 38074 ], [ 38219 ], [ 1009 ], [ 18313 ], [ 10 ], [ 1906 ], [ 10627 ], [ 5815 ], [ 3490389.2350042677 ], [ 20396.083232355562 ], [ 1972565.054269381 ], [ 1696844.705623965 ] ], "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "hovertemplate": "labels=%{label}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = df_cases[df_cases['Date']=='8/21/20'] # take the last day of observation so cumulative values are maximum\n", "fig = px.sunburst(df, path=['IncomeLevel', 'Country_WB'], values=df['Value'],\n", " color=df['Value'], color_continuous_scale='Reds', \n", " color_continuous_midpoint=np.average(df['Value'], weights=df['Value']),\n", " title = 'Confirmed cases by income level and countries
by August 21, 2020')\n", "fig.show()\n", "\n", "fig.write_html(\"Confirmed_income&country.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first it could look like there is a correlation, as 80% of cases are confirmed in countries where income level is higher than medium. But it is importnant to note, that the number of confirmed cases is lower than the number of actual cases at all times, the main reason for that is limited testing. On one hand, this especially could make effect on COVID-19 statistics in lower income countries where the virus is harder to diagnosed due to various limitations. On the other hand, low income countries population is less globaly mobile and this factor is probably slowing down the spreading of virus there in comparison with high income countries. \n", "\n", "In any case there are lots of controversial effects from different groups of factors and it is too early to make conclusions at this stage given the available statistics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Deaths statistics analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §2.1. Cleaning and preparing for animation: deaths" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [], "source": [ "import pandas as pd\n", "import pycountry\n", "\n", "df_deaths=pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\")\n", "\n", "# Aggregate the dataset\n", "df_deaths = df_deaths.drop(columns=['Province/State','Lat','Long'])\n", "df_deaths = df_deaths.groupby('Country/Region').agg('sum')\n", "date_list = list(df_deaths.columns)\n", "\n", "df_deaths['Country'] = df_deaths.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several countries' names are written differently than pycountry expects, so I change their names to match and get the code" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "df_deaths.loc[df_deaths.Country==\"Burma\",'Country']='Myanmar'\n", "df_deaths.loc[df_deaths.Country==\"Brunei\",'Country']='Brunei Darussalam'\n", "df_deaths.loc[df_deaths.Country==\"Iran\",'Country']='Iran, Islamic Republic of'\n", "df_deaths.loc[df_deaths.Country==\"Congo (Brazzaville)\",'Country']='Congo, The Democratic Republic of the'\n", "df_deaths.loc[df_deaths.Country==\"Congo (Kinshasa)\",'Country']='Republic of the Congo'\n", "df_deaths.loc[df_deaths.Country==\"Cote d'Ivoire\",'Country']=\"Côte d'Ivoire\"\n", "df_deaths.loc[df_deaths.Country==\"Korea, South\",'Country']=\"Korea, Republic of\"\n", "df_deaths.loc[df_deaths.Country==\"Syria\",'Country']=\"Syrian Arab Republic\"\n", "df_deaths.loc[df_deaths.Country==\"Taiwan*\",'Country']=\"Taiwan, Province of China\"\n", "df_deaths.loc[df_deaths.Country==\"Russia\",'Country']='Russian Federation'\n", "df_deaths.loc[df_deaths.Country==\"West Bank and Gaza\",'Country']='Palestine, State of'\n", "df_deaths.loc[df_deaths.Country==\"Venezuela\",'Country']='Venezuela, Bolivarian Republic of'\n", "df_deaths.loc[df_deaths.Country==\"US\",'Country']='United States'\n", "df_deaths.loc[df_deaths.Country==\"Laos\",'Country']=\"Lao People's Democratic Republic\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As soon as names are unified, I can add their ISO-3 codes." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "df_deaths['ISO-3'] = df_deaths['Country'].apply(get_country_code)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryISO-3variablevalue
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Country, ISO-3, variable, value]\n", "Index: []" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Transform the dataset in a long format\n", "df_deaths = pd.melt(df_deaths, id_vars=['Country','ISO-3'], value_vars=date_list)\n", "df_deaths.head(0)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryISO-3DateValue
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Country, ISO-3, Date, Value]\n", "Index: []" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_deaths = df_deaths.rename(columns={\"variable\": \"Date\",\"value\": \"Value\"})\n", "df_deaths.head(0)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "#add Kosovo ISO-3 code as it is not listed in pycountry dictionary\n", "df_deaths.loc[df_deaths.Country==\"Kosovo\",'ISO-3']=\"XKX\"\n", "df_deaths = df_deaths.dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §2.2. Animation of the map over time: deaths" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "customdata": [ [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 17 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ] ], "geo": "geo", "hovertemplate": "%{hovertext}

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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.express as px\n", "df = df_deaths\n", "fig = px.choropleth(df, # input dataframe\n", " locationmode='ISO-3', # set of locations used to map 'locations' \n", " locations=\"ISO-3\", # identify country by code\n", " color=np.log10(df['Value']), # identify values and replace linear scale with logarithmic scale\n", " hover_name=\"Country\", # identify column to add as name to hover information\n", " animation_frame=\"Date\", # identify date column\n", " projection=\"equirectangular\", # select projection\n", " hover_data=[df['Value']], # hover text\n", " center = {\"lat\": 14.883333, \"lon\": 5.266667},# set map center\n", " color_continuous_scale=px.colors.sequential.Reds, # set color scale, \"_r\" to reverse color\n", " range_color=[0,round(np.log10(df['Value']).max(),2)], # set the range of dataset\n", " )\n", "\n", "#customize layout\n", "fig.update_layout(\n", " title_text='Deaths from COVID-19 by country over time
January 22, 2020 - August 21, 2020',\n", " geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'),\n", " \n", " annotations = [dict(\n", " x=0.8,\n", " y=0.0,\n", " xref='paper',\n", " yref='paper',\n", " text='Source: \\\n", " Johns Hopkins University & Medicine',\n", " showarrow = False\n", " )],\n", " \n", " #customize colorbar\n", " coloraxis_colorbar=dict(\n", " title='Deaths',\n", " tickvals=[0, 1, 2, 3, 4, 5, 5.3010299957], #customize colorbar title and ticks values\n", " ticktext = ['1', '10', '100', '1K', '10K', '100K', '200K'] #replace log10 colorbar ticks text\n", " ) \n", " )\n", "fig.show() \n", "fig.write_html(\"Deaths_map.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As of August 2020 maximum number of lethal end cases were registered in USA. Mongolia has not reported COVID-19 deaths." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max number of deaths: 176353\n" ] } ], "source": [ "print(\"Max number of deaths:\", df_deaths['Value'].max())" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryISO-3DateValueRegionIncomeLevelCountry_WB
0AfghanistanAFG1/22/200South AsiaLow incomeAfghanistan
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" ], "text/plain": [ " Country ISO-3 Date Value Region IncomeLevel Country_WB\n", "0 Afghanistan AFG 1/22/20 0 South Asia Low income Afghanistan" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_deaths = df_deaths.merge(df_convert,on='ISO-3')\n", "df_deaths.head(1)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Country False\n", "ISO-3 False\n", "Date False\n", "Value False\n", "Region False\n", "IncomeLevel False\n", "Country_WB False\n", "dtype: bool" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_deaths.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §2.3. Structure by region and income level: deaths" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interactive sunburst graph shows countries within world's regions where the lethal end cases of COVID-19 were reported by August 20, 2020. " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "branchvalues": "total", "customdata": [ [ 1385 ], [ 238 ], [ 1411 ], [ 53 ], [ 93 ], [ 3 ], [ 6517 ], [ 836 ], [ 472 ], [ 729 ], [ 510 ], [ 23 ], [ 179 ], [ 3822 ], [ 7 ], [ 627 ], [ 9976 ], [ 5 ], [ 39 ], [ 4305 ], [ 515 ], [ 3 ], [ 112304 ], [ 3 ], [ 532 ], [ 55 ], [ 1 ], [ 37 ], [ 408 ], [ 9097 ], [ 61 ], [ 76 ], [ 10671 ], [ 4709 ], [ 16183 ], [ 7 ], [ 77 ], [ 333 ], [ 168 ], [ 88 ], [ 20 ], [ 406 ], [ 112 ], [ 621 ], [ 59 ], [ 1505 ], [ 6200 ], [ 5212 ], [ 640 ], [ 83 ], [ 63 ], [ 81 ], [ 620 ], [ 1 ], [ 334 ], [ 30434 ], [ 53 ], [ 81 ], [ 17 ], [ 9263 ], [ 261 ], [ 235 ], [ 2506 ], [ 53 ], [ 33 ], [ 29 ], [ 196 ], [ 1619 ], [ 609 ], [ 10 ], [ 54849 ], [ 6418 ], [ 20264 ], [ 6208 ], [ 1776 ], [ 795 ], [ 35418 ], [ 15 ], [ 1157 ], [ 11 ], [ 1415 ], [ 516 ], [ 309 ], [ 390 ], [ 509 ], [ 1498 ], [ 33 ], [ 113 ], [ 30 ], [ 82 ], [ 173 ], [ 1 ], [ 82 ], [ 124 ], [ 177 ], [ 165 ], [ 125 ], [ 24 ], [ 125 ], [ 9 ], [ 158 ], [ 10 ], [ 59106 ], [ 921 ], [ 4 ], [ 81 ], [ 775 ], [ 20 ], [ 6 ], [ 41 ], [ 126 ], [ 6215 ], [ 22 ], [ 133 ], [ 69 ], [ 992 ], [ 554 ], [ 264 ], [ 609 ], [ 6209 ], [ 120 ], [ 1844 ], [ 4 ], [ 170 ], [ 26834 ], [ 2883 ], [ 1925 ], [ 1788 ], [ 193 ], [ 247 ], [ 3154 ], [ 16058 ], [ 11 ], [ 42 ], [ 15 ], [ 3548 ], [ 261 ], [ 689 ], [ 69 ], [ 27 ], [ 33 ], [ 129 ], [ 93 ], [ 12618 ], [ 47 ], [ 28813 ], [ 11 ], [ 812 ], [ 55 ], [ 5805 ], [ 1998 ], [ 82 ], [ 7 ], [ 66 ], [ 21 ], [ 58 ], [ 27 ], [ 12 ], [ 63 ], [ 6058 ], [ 19 ], [ 2225 ], [ 369 ], [ 41489 ], [ 174255 ], [ 41 ], [ 252 ], [ 311 ], [ 25 ], [ 1 ], [ 541 ], [ 274 ], [ 151 ], [ 4520.827437446074 ], [ 24701.94526116494 ], [ 68809.51420396972 ], [ 11961.91710108382 ], [ 166060.6943693006 ], [ 46119.09439075061 ], [ 8409.13129375162 ] ], "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "hovertemplate": "labels=%{label}
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by August 20, 2020',\n", " color_continuous_midpoint=round(np.average(df.Value, weights = df.Value),1))\n", "fig.show()\n", "fig.write_html(\"Deaths_region&country.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is there a pattern in terms of virus spread between different regions of income? The sunburst graph shows that 'High income' countries and 'Upper medium income' countries cover 41% and 39% of total COVID-19 cases respectively; Lower middle income countries cover less than 19.5% of total COVID-19 cases; virus spread in low income countries are not that significant yet, or maybe it just was not diagnosed properly. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "branchvalues": "total", "customdata": [ [ 1385 ], [ 238 ], [ 1411 ], [ 53 ], [ 93 ], [ 3 ], [ 6517 ], [ 836 ], [ 472 ], [ 729 ], [ 510 ], [ 23 ], [ 179 ], [ 3822 ], [ 7 ], [ 627 ], [ 9976 ], [ 5 ], [ 39 ], [ 4305 ], [ 515 ], [ 3 ], [ 112304 ], [ 3 ], [ 532 ], [ 55 ], [ 1 ], [ 37 ], [ 408 ], [ 9097 ], [ 61 ], [ 76 ], [ 10671 ], [ 4709 ], [ 16183 ], [ 7 ], [ 77 ], [ 333 ], [ 168 ], [ 88 ], [ 20 ], [ 406 ], [ 112 ], [ 621 ], [ 59 ], [ 1505 ], [ 6200 ], [ 5212 ], [ 640 ], [ 83 ], [ 63 ], [ 81 ], [ 620 ], [ 1 ], [ 334 ], [ 30434 ], [ 53 ], [ 81 ], [ 17 ], [ 9263 ], [ 261 ], [ 235 ], [ 2506 ], [ 53 ], [ 33 ], [ 29 ], [ 196 ], [ 1619 ], [ 609 ], [ 10 ], [ 54849 ], [ 6418 ], [ 20264 ], [ 6208 ], [ 1776 ], [ 795 ], [ 35418 ], [ 15 ], [ 1157 ], [ 11 ], [ 1415 ], [ 516 ], [ 309 ], [ 390 ], [ 509 ], [ 1498 ], [ 33 ], [ 113 ], [ 30 ], [ 82 ], [ 173 ], [ 1 ], [ 82 ], [ 124 ], [ 177 ], [ 165 ], [ 125 ], [ 24 ], [ 125 ], [ 9 ], [ 158 ], [ 10 ], [ 59106 ], [ 921 ], [ 4 ], [ 81 ], [ 775 ], [ 20 ], [ 6 ], [ 41 ], [ 126 ], [ 6215 ], [ 22 ], [ 133 ], [ 69 ], [ 992 ], [ 554 ], [ 264 ], [ 609 ], [ 6209 ], [ 120 ], [ 1844 ], [ 4 ], [ 170 ], [ 26834 ], [ 2883 ], [ 1925 ], [ 1788 ], [ 193 ], [ 247 ], [ 3154 ], [ 16058 ], [ 11 ], [ 42 ], [ 15 ], [ 3548 ], [ 261 ], [ 689 ], [ 69 ], [ 27 ], [ 33 ], [ 129 ], [ 93 ], [ 12618 ], [ 47 ], [ 28813 ], [ 11 ], [ 812 ], [ 55 ], [ 5805 ], [ 1998 ], [ 82 ], [ 7 ], [ 66 ], [ 21 ], [ 58 ], [ 27 ], [ 12 ], [ 63 ], [ 6058 ], [ 19 ], [ 2225 ], [ 369 ], [ 41489 ], [ 174255 ], [ 41 ], [ 252 ], [ 311 ], [ 25 ], [ 1 ], [ 541 ], [ 274 ], [ 151 ], [ 92173.82508124344 ], [ 681.330755502677 ], [ 34456.55266748834 ], [ 58246.58610065376 ] ], "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "hovertemplate": "labels=%{label}
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"gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Deaths from COVID-19 by income level and country
by August 20, 2020" } } }, "text/html": [ "
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\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# filter the rows for the last day of observation so cumulative values would be maximum and latest\n", "df = df_deaths[df_deaths['Date'] == '8/20/20']\n", "\n", "#exclude rows with zero values of 12 countries with no reported deaths \n", "df = df[df['Value'] != 0] \n", "\n", "fig = px.sunburst(df, path=['IncomeLevel', 'Country'], values=df.Value,\n", " color = df.Value, color_continuous_scale='Reds', \n", " title = 'Deaths from COVID-19 by income level and country
by August 20, 2020',\n", " color_continuous_midpoint=round(np.average(df.Value, weights = df.Value),1))\n", "fig.show()\n", "fig.write_html(\"Deaths_income&country.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Recovered patients" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §3.1. Cleaning and preparing for animation: recovery" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import pycountry\n", "\n", "df_Recovered=pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv\")\n", "# Aggregate the dataset\n", "df_Recovered = df_Recovered.drop(columns=['Province/State','Lat','Long'])\n", "df_Recovered = df_Recovered.groupby('Country/Region').agg('sum')\n", "date_list = list(df_Recovered.columns)\n", "df_Recovered['Country'] = df_Recovered.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several countries' names are written differently than pycountry expects, so I change their names to match and get the code" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "df_Recovered.loc[df_Recovered.Country==\"Burma\",'Country']='Myanmar'\n", "df_Recovered.loc[df_Recovered.Country==\"Brunei\",'Country']='Brunei Darussalam'\n", "df_Recovered.loc[df_Recovered.Country==\"Iran\",'Country']='Iran, Islamic Republic of'\n", "df_Recovered.loc[df_Recovered.Country==\"Congo (Brazzaville)\",'Country']='Congo, The Democratic Republic of the'\n", "df_Recovered.loc[df_Recovered.Country==\"Congo (Kinshasa)\",'Country']='Republic of the Congo'\n", "df_Recovered.loc[df_Recovered.Country==\"Cote d'Ivoire\",'Country']=\"Côte d'Ivoire\"\n", "df_Recovered.loc[df_Recovered.Country==\"Korea, South\",'Country']=\"Korea, Republic of\"\n", "df_Recovered.loc[df_Recovered.Country==\"Syria\",'Country']=\"Syrian Arab Republic\"\n", "df_Recovered.loc[df_Recovered.Country==\"Taiwan*\",'Country']=\"Taiwan, Province of China\"\n", "df_Recovered.loc[df_Recovered.Country==\"Russia\",'Country']='Russian Federation'\n", "df_Recovered.loc[df_Recovered.Country==\"West Bank and Gaza\",'Country']='Palestine, State of'\n", "df_Recovered.loc[df_Recovered.Country==\"Venezuela\",'Country']='Venezuela, Bolivarian Republic of'\n", "df_Recovered.loc[df_Recovered.Country==\"US\",'Country']='United States'\n", "df_Recovered.loc[df_Recovered.Country==\"Laos\",'Country']=\"Lao People's Democratic Republic\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As soon as names are unified, I can add their ISO-3 codes." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "df_Recovered['ISO-3'] = df_Recovered['Country'].apply(get_country_code)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/201/31/20...8/15/208/16/208/17/208/18/208/19/208/20/208/21/208/22/20CountryISO-3
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Albania0000000000...37463794381638713928398640964184AlbaniaALB
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2 rows × 216 columns

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" ], "text/plain": [ " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", "Country/Region \n", "Afghanistan 0 0 0 0 0 0 0 \n", "Albania 0 0 0 0 0 0 0 \n", "\n", " 1/29/20 1/30/20 1/31/20 ... 8/15/20 8/16/20 8/17/20 \\\n", "Country/Region ... \n", "Afghanistan 0 0 0 ... 27166 27166 27166 \n", "Albania 0 0 0 ... 3746 3794 3816 \n", "\n", " 8/18/20 8/19/20 8/20/20 8/21/20 8/22/20 Country \\\n", "Country/Region \n", "Afghanistan 27166 27166 27681 28016 28016 Afghanistan \n", "Albania 3871 3928 3986 4096 4184 Albania \n", "\n", " ISO-3 \n", "Country/Region \n", "Afghanistan AFG \n", "Albania ALB \n", "\n", "[2 rows x 216 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View data structure\n", "df_Recovered.head(2)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryISO-3variablevalue
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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Country, ISO-3, variable, value]\n", "Index: []" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Transform the dataset in a long format\n", "df_Recovered = pd.melt(df_Recovered, id_vars=['Country','ISO-3'], value_vars=date_list)\n", "df_Recovered.head(0)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "df_Recovered = df_Recovered.rename(columns={\"variable\": \"Date\",\"value\": \"Value\"})" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "#add Kosovo ISO-3 code as it is not listed in pycountry dictionary\n", "df_Recovered.loc[df_Recovered.Country==\"Kosovo\",'ISO-3']=\"XKX\"\n", "df_Recovered = df_Recovered.dropna()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(39590, 4)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View data shape\n", "df_Recovered.shape " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As of August 20, 2020 maximum number of recovered patients in one country were registered in Brazil." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Max number of recovered: 2913966\n" ] } ], "source": [ "print(\"Max number of recovered:\", df_Recovered.Value.max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §3.2. Animation of the map over time: recovery" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "customdata": [ [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 28 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ], [ 0 ] ], "geo": "geo", "hovertemplate": "%{hovertext}

Date=1/22/20
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"#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Recovered from COVID-19 by country over time
January 22, 2020 - August 21, 2020" }, "updatemenus": [ { "buttons": [ { "args": [ null, { "frame": { "duration": 500, "redraw": true }, "fromcurrent": true, "mode": "immediate", "transition": { "duration": 500, "easing": "linear" } } ], "label": "▶", "method": "animate" }, { "args": [ [ null ], { "frame": { "duration": 0, "redraw": true }, "fromcurrent": true, "mode": "immediate", "transition": { "duration": 0, "easing": "linear" } } ], "label": "◼", "method": "animate" } ], "direction": "left", "pad": { "r": 10, "t": 70 }, "showactive": false, "type": "buttons", "x": 0.1, "xanchor": "right", "y": 0, "yanchor": "top" } ] } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.express as px\n", "df = df_Recovered\n", "fig = px.choropleth(df, # input dataframe\n", " locationmode='ISO-3', # set of locations used to map 'locations' \n", " locations=\"ISO-3\", # identify country by code\n", " color=np.log10(df['Value']), # identify values and replace linear scale with logarithmic scale\n", " hover_name=\"Country\", # identify column to add as name to hover information\n", " animation_frame=\"Date\", # identify date column\n", " projection=\"equirectangular\", # select projection\n", " hover_data=[df['Value']], # hover text\n", " center = {\"lat\": 14.883333, \"lon\": 5.266667},# set map center\n", " color_continuous_scale=px.colors.sequential.Reds, # set color scale, \"_r\" to reverse color\n", " range_color=[0,round(np.log10(df['Value']).max(),2)], # set the range of dataset\n", " )\n", "\n", "#customize layout\n", "fig.update_layout(\n", " title_text='Recovered from COVID-19 by country over time
January 22, 2020 - August 21, 2020',\n", " geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'),\n", " \n", " annotations = [dict(\n", " x=0.8,\n", " y=0.0,\n", " xref='paper',\n", " yref='paper',\n", " text='Source: \\\n", " Johns Hopkins University & Medicine',\n", " showarrow = False\n", " )],\n", " \n", " #customize colorbar\n", " coloraxis_colorbar=dict(\n", " title='Recovered',\n", " tickvals=[0,1,2, 3, 4, 5, 6, 6.5], #customize colorbar title and ticks values\n", " ticktext = ['1','10','100', '1K', '10K', '100K', '1M', '3M'] #replace log10 colorbar ticks text\n", " ) \n", " )\n", "fig.show() \n", "fig.write_html(\"Recovered_map.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interactive map shows where are located the most number of recovery cases. It is Brazil (2.6 mln), India (1.9 mln), United States (1.8 mln), Russia (0.7 mln) and South Africa (0.5 mln)." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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0AfghanistanAFG1/22/200South AsiaLow incomeAfghanistan
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" ], "text/plain": [ " Country ISO-3 Date Value Region IncomeLevel Country_WB\n", "0 Afghanistan AFG 1/22/20 0 South Asia Low income Afghanistan" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_Recovered = df_Recovered.merge(df_convert,on='ISO-3')\n", "df_Recovered.head(1)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Country False\n", "ISO-3 False\n", "Date False\n", "Value False\n", "Region False\n", "IncomeLevel False\n", "Country_WB False\n", "dtype: bool" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_Recovered.isnull().any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §3.3. Structure by region and income level: recovery" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interactive sunburst graph shows countries within world's regions where the recovery cases of COVID-19 were reported by August 21, 2020. " ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "branchvalues": "total", "customdata": [ [ 28016 ], [ 4096 ], [ 28587 ], [ 875 ], [ 804 ], [ 89 ], [ 239806 ], [ 35693 ], [ 18458 ], [ 21260 ], [ 32682 ], [ 221 ], [ 45166 ], [ 172615 ], [ 124 ], [ 68256 ], [ 18165 ], [ 43 ], [ 1705 ], [ 110 ], [ 42141 ], [ 11157 ], [ 136 ], [ 2855558 ], [ 139 ], [ 10282 ], [ 1034 ], [ 336 ], [ 2498 ], [ 263 ], [ 16540 ], [ 112381 ], [ 1755 ], [ 869 ], [ 367897 ], [ 83793 ], [ 348940 ], [ 396 ], [ 1625 ], [ 10159 ], [ 5584 ], [ 2951 ], [ 878 ], [ 16040 ], [ 14820 ], [ 14258 ], [ 5233 ], [ 18 ], [ 59132 ], [ 87730 ], [ 64318 ], [ 11781 ], [ 2713 ], [ 274 ], [ 2011 ], [ 2682 ], [ 13913 ], [ 20 ], [ 7100 ], [ 84981 ], [ 6734 ], [ 455 ], [ 1128 ], [ 206656 ], [ 41408 ], [ 1347 ], [ 24 ], [ 55314 ], [ 7648 ], [ 1015 ], [ 433 ], [ 5447 ], [ 8271 ], [ 3681 ], [ 1920 ], [ 2222577 ], [ 102991 ], [ 305866 ], [ 140446 ], [ 23364 ], [ 77785 ], [ 204960 ], [ 788 ], [ 46264 ], [ 1262 ], [ 89712 ], [ 18157 ], [ 14169 ], [ 8149 ], [ 71264 ], [ 35831 ], [ 20 ], [ 1093 ], [ 3204 ], [ 472 ], [ 803 ], [ 1053 ], [ 88 ], [ 1755 ], [ 6969 ], [ 13206 ], [ 2929 ], [ 8945 ], [ 4012 ], [ 2007 ], [ 854 ], [ 6186 ], [ 334 ], [ 448344 ], [ 22683 ], [ 116 ], [ 288 ], [ 3281 ], [ 34199 ], [ 1406 ], [ 337 ], [ 2457 ], [ 18214 ], [ 600 ], [ 1538 ], [ 2913 ], [ 1083 ], [ 37885 ], [ 9977 ], [ 9150 ], [ 78386 ], [ 273579 ], [ 11102 ], [ 59722 ], [ 198 ], [ 7007 ], [ 380730 ], [ 114519 ], [ 41029 ], [ 40473 ], [ 113216 ], [ 8920 ], [ 34523 ], [ 759639 ], [ 1712 ], [ 17 ], [ 25 ], [ 56 ], [ 657 ], [ 830 ], [ 277067 ], [ 8165 ], [ 126 ], [ 1542 ], [ 53651 ], [ 2045 ], [ 2079 ], [ 2396 ], [ 500102 ], [ 1290 ], [ 150376 ], [ 2789 ], [ 6476 ], [ 2498 ], [ 33900 ], [ 475 ], [ 457 ], [ 7040 ], [ 183 ], [ 3219 ], [ 25 ], [ 891 ], [ 162 ], [ 1420 ], [ 235569 ], [ 1199 ], [ 52769 ], [ 58296 ], [ 1544 ], [ 1965056 ], [ 1249 ], [ 33989 ], [ 27306 ], [ 545 ], [ 8 ], [ 1058 ], [ 9126 ], [ 4587 ], [ 80910.79708295634 ], [ 328643.2717020763 ], [ 1757655.1168546157 ], [ 175622.00682670117 ], [ 1864833.7216950501 ], [ 1853708.151186372 ], [ 338201.7188520611 ] ], "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "hovertemplate": "labels=%{label}
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\n", " \n", " \n", "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import plotly.express as px\n", "\n", "# filter the rows for the last day of observation so cumulative values would be maximum and latest\n", "df = df_Recovered[df_Recovered['Date'] == '8/21/20']\n", "\n", "#exclude rows with zero values of 12 countries with no deaths reported\n", "df = df[df['Value'] != 0] \n", "\n", "fig = px.sunburst(df, path = ['Region', 'Country'], values = df.Value,\n", " color = df.Value, color_continuous_scale='Reds', \n", " title = 'Recovered from COVID-19 by regions and countries
by August 21, 2020',\n", " color_continuous_midpoint=round(np.average(df.Value, weights = df.Value),1))\n", "fig.show()\n", "fig.write_html(\"Recovered_region&country.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is there a pattern in terms of virus spread between different regions of income? The sunburst graph shows that 'High income' countries and 'Upper medium income' countries cover 41% and 39% of total COVID-19 cases respectively; Lower middle income countries cover less than 19.5% of total COVID-19 cases; virus spread in low income countries are not that significant yet, or maybe it just was not diagnosed properly. " ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "branchvalues": "total", "customdata": [ [ 28016 ], [ 4096 ], [ 28587 ], [ 875 ], [ 804 ], [ 89 ], [ 239806 ], [ 35693 ], [ 18458 ], [ 21260 ], [ 32682 ], [ 221 ], [ 45166 ], [ 172615 ], [ 124 ], [ 68256 ], [ 18165 ], [ 43 ], [ 1705 ], [ 110 ], [ 42141 ], [ 11157 ], [ 136 ], [ 2855558 ], [ 139 ], [ 10282 ], [ 1034 ], [ 336 ], [ 2498 ], [ 263 ], [ 16540 ], [ 112381 ], [ 1755 ], [ 869 ], [ 367897 ], [ 83793 ], [ 348940 ], [ 396 ], [ 1625 ], [ 10159 ], [ 5584 ], [ 2951 ], [ 878 ], [ 16040 ], [ 14820 ], [ 14258 ], [ 5233 ], [ 18 ], [ 59132 ], [ 87730 ], [ 64318 ], [ 11781 ], [ 2713 ], [ 274 ], [ 2011 ], [ 2682 ], [ 13913 ], [ 20 ], [ 7100 ], [ 84981 ], [ 6734 ], [ 455 ], [ 1128 ], [ 206656 ], [ 41408 ], [ 1347 ], [ 24 ], [ 55314 ], [ 7648 ], [ 1015 ], [ 433 ], [ 5447 ], [ 8271 ], [ 3681 ], [ 1920 ], [ 2222577 ], [ 102991 ], [ 305866 ], [ 140446 ], [ 23364 ], [ 77785 ], [ 204960 ], [ 788 ], [ 46264 ], [ 1262 ], [ 89712 ], [ 18157 ], [ 14169 ], [ 8149 ], [ 71264 ], [ 35831 ], [ 20 ], [ 1093 ], [ 3204 ], [ 472 ], [ 803 ], [ 1053 ], [ 88 ], [ 1755 ], [ 6969 ], [ 13206 ], [ 2929 ], [ 8945 ], [ 4012 ], [ 2007 ], [ 854 ], [ 6186 ], [ 334 ], [ 448344 ], [ 22683 ], [ 116 ], [ 288 ], [ 3281 ], [ 34199 ], [ 1406 ], [ 337 ], [ 2457 ], [ 18214 ], [ 600 ], [ 1538 ], [ 2913 ], [ 1083 ], [ 37885 ], [ 9977 ], [ 9150 ], [ 78386 ], [ 273579 ], [ 11102 ], [ 59722 ], [ 198 ], [ 7007 ], [ 380730 ], [ 114519 ], [ 41029 ], [ 40473 ], [ 113216 ], [ 8920 ], [ 34523 ], [ 759639 ], [ 1712 ], [ 17 ], [ 25 ], [ 56 ], [ 657 ], [ 830 ], [ 277067 ], [ 8165 ], [ 126 ], [ 1542 ], [ 53651 ], [ 2045 ], [ 2079 ], [ 2396 ], [ 500102 ], [ 1290 ], [ 150376 ], [ 2789 ], [ 6476 ], [ 2498 ], [ 33900 ], [ 475 ], [ 457 ], [ 7040 ], [ 183 ], [ 3219 ], [ 25 ], [ 891 ], [ 162 ], [ 1420 ], [ 235569 ], [ 1199 ], [ 52769 ], [ 58296 ], [ 1544 ], [ 1965056 ], [ 1249 ], [ 33989 ], [ 27306 ], [ 545 ], [ 8 ], [ 1058 ], [ 9126 ], [ 4587 ], [ 988275.0810035401 ], [ 12735.774402224282 ], [ 1521338.0335548304 ], [ 1396410.8726990526 ] ], "domain": { "x": [ 0, 1 ], "y": [ 0, 1 ] }, "hovertemplate": "labels=%{label}
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by August 21, 2020" } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# filter the rows for the last day of observation so cumulative values would be maximum and latest\n", "df = df_Recovered[df_Recovered['Date'] == '8/21/20']\n", "\n", "#exclude rows with zero values of 12 countries with no reported deaths \n", "df = df[df['Value'] != 0] \n", "\n", "fig = px.sunburst(df, path=['IncomeLevel', 'Country'], values=df.Value,\n", " color = df.Value, color_continuous_scale='Reds', \n", " title = 'Recovered from COVID-19 by income level and country
by August 21, 2020',\n", " color_continuous_midpoint=round(np.average(df.Value, weights = df.Value),1))\n", "fig.show()\n", "fig.write_html(\"Recovered_income&country.html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Calculating additional data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §4.1. Importing, cleaning and calculating" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to make meaningful comparison of deaths from virus between the countries I need to import population data first, and calculate deaths per million ratio." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(4153, 12)" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd #to work with tabular data\n", "import requests #to access the csv from the url string\n", "import io #to read the csv directly from the url string\n", "\n", "url_pop = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/UID_ISO_FIPS_LookUp_Table.csv\"\n", "raw=requests.get(url_pop).content\n", "df_pop=pd.read_csv(io.StringIO(raw.decode('utf-8')))\n", "\n", "# View data shape\n", "df_pop.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I expected to see about 200 rows in the dataset (1 row per country), but its shape contains 4153 rows. The reason for that is, as we can see below, that it contains not only population of the countries, but mostly population of provinces, states and cities for some countries." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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UIDiso2iso3code3FIPSAdmin2Province_StateCountry_RegionLatLong_Combined_KeyPopulation
7815217CLCHL152.0NaNNaNUnknownChileNaNNaNUnknown, ChileNaN
79170COCOL170.0NaNNaNNaNColombia4.5709-74.2973Colombia50882884.0
8017001COCOL170.0NaNNaNAmazonasColombia-1.4429-71.5724Amazonas, Colombia76589.0
8117002COCOL170.0NaNNaNAntioquiaColombia7.1986-75.3412Antioquia, Colombia6407102.0
8217003COCOL170.0NaNNaNAraucaColombia7.0762-70.7105Arauca, Colombia262174.0
8317004COCOL170.0NaNNaNAtlanticoColombia10.6966-74.8741Atlantico, Colombia2535517.0
8417005COCOL170.0NaNNaNBolivarColombia8.6704-74.0300Bolivar, Colombia2070110.0
8517006COCOL170.0NaNNaNBoyacaColombia5.4545-73.3620Boyaca, Colombia1217376.0
8617007COCOL170.0NaNNaNCaldasColombia5.2983-75.2479Caldas, Colombia998255.0
8717008COCOL170.0NaNNaNCapital DistrictColombia4.7110-74.0721Capital District, Colombia7412566.0
8817009COCOL170.0NaNNaNCaquetaColombia0.8699-73.8419Caqueta, Colombia401489.0
8917010COCOL170.0NaNNaNCasanareColombia5.7589-71.5724Casanare, Colombia420504.0
9017011COCOL170.0NaNNaNCaucaColombia2.7050-76.8260Cauca, Colombia1464488.0
9117012COCOL170.0NaNNaNCesarColombia9.3373-73.6536Cesar, Colombia1200574.0
9217013COCOL170.0NaNNaNChocoColombia5.2528-76.8260Choco, Colombia534826.0
9317014COCOL170.0NaNNaNCordobaColombia8.0493-75.5740Cordoba, Colombia1784783.0
9417015COCOL170.0NaNNaNCundinamarcaColombia5.0260-74.0300Cundinamarca, Colombia2919060.0
9517016COCOL170.0NaNNaNGuainiaColombia2.5854-68.5247Guainia, Colombia48114.0
9617017COCOL170.0NaNNaNGuaviareColombia1.0654-73.2603Guaviare, Colombia82767.0
9717018COCOL170.0NaNNaNHuilaColombia2.5359-75.5277Huila, Colombia1100386.0
9817019COCOL170.0NaNNaNLa GuajiraColombia11.3548-72.5205La Guajira, Colombia880560.0
9917020COCOL170.0NaNNaNMagdalenaColombia10.4113-74.4057Magdalena, Colombia1341746.0
\n", "
" ], "text/plain": [ " UID iso2 iso3 code3 FIPS Admin2 Province_State Country_Region \\\n", "78 15217 CL CHL 152.0 NaN NaN Unknown Chile \n", "79 170 CO COL 170.0 NaN NaN NaN Colombia \n", "80 17001 CO COL 170.0 NaN NaN Amazonas Colombia \n", "81 17002 CO COL 170.0 NaN NaN Antioquia Colombia \n", "82 17003 CO COL 170.0 NaN NaN Arauca Colombia \n", "83 17004 CO COL 170.0 NaN NaN Atlantico Colombia \n", "84 17005 CO COL 170.0 NaN NaN Bolivar Colombia \n", "85 17006 CO COL 170.0 NaN NaN Boyaca Colombia \n", "86 17007 CO COL 170.0 NaN NaN Caldas Colombia \n", "87 17008 CO COL 170.0 NaN NaN Capital District Colombia \n", "88 17009 CO COL 170.0 NaN NaN Caqueta Colombia \n", "89 17010 CO COL 170.0 NaN NaN Casanare Colombia \n", "90 17011 CO COL 170.0 NaN NaN Cauca Colombia \n", "91 17012 CO COL 170.0 NaN NaN Cesar Colombia \n", "92 17013 CO COL 170.0 NaN NaN Choco Colombia \n", "93 17014 CO COL 170.0 NaN NaN Cordoba Colombia \n", "94 17015 CO COL 170.0 NaN NaN Cundinamarca Colombia \n", "95 17016 CO COL 170.0 NaN NaN Guainia Colombia \n", "96 17017 CO COL 170.0 NaN NaN Guaviare Colombia \n", "97 17018 CO COL 170.0 NaN NaN Huila Colombia \n", "98 17019 CO COL 170.0 NaN NaN La Guajira Colombia \n", "99 17020 CO COL 170.0 NaN NaN Magdalena Colombia \n", "\n", " Lat Long_ Combined_Key Population \n", "78 NaN NaN Unknown, Chile NaN \n", "79 4.5709 -74.2973 Colombia 50882884.0 \n", "80 -1.4429 -71.5724 Amazonas, Colombia 76589.0 \n", "81 7.1986 -75.3412 Antioquia, Colombia 6407102.0 \n", "82 7.0762 -70.7105 Arauca, Colombia 262174.0 \n", "83 10.6966 -74.8741 Atlantico, Colombia 2535517.0 \n", "84 8.6704 -74.0300 Bolivar, Colombia 2070110.0 \n", "85 5.4545 -73.3620 Boyaca, Colombia 1217376.0 \n", "86 5.2983 -75.2479 Caldas, Colombia 998255.0 \n", "87 4.7110 -74.0721 Capital District, Colombia 7412566.0 \n", "88 0.8699 -73.8419 Caqueta, Colombia 401489.0 \n", "89 5.7589 -71.5724 Casanare, Colombia 420504.0 \n", "90 2.7050 -76.8260 Cauca, Colombia 1464488.0 \n", "91 9.3373 -73.6536 Cesar, Colombia 1200574.0 \n", "92 5.2528 -76.8260 Choco, Colombia 534826.0 \n", "93 8.0493 -75.5740 Cordoba, Colombia 1784783.0 \n", "94 5.0260 -74.0300 Cundinamarca, Colombia 2919060.0 \n", "95 2.5854 -68.5247 Guainia, Colombia 48114.0 \n", "96 1.0654 -73.2603 Guaviare, Colombia 82767.0 \n", "97 2.5359 -75.5277 Huila, Colombia 1100386.0 \n", "98 11.3548 -72.5205 La Guajira, Colombia 880560.0 \n", "99 10.4113 -74.4057 Magdalena, Colombia 1341746.0 " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View a slice of loaded data\n", "df_pop[78:100]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To avoid double counting I need to clean the dataset from excessive information. To do that, I filter only the rows with values NaN in column \"Province_State\" using the isnull function. After that I can simply drop \"UID\", \"iso2\", \"code3\", \"FIPS\", \"Admin2\", \"Province_State\", \"Combined_Key\" columns." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "df_pop = df_pop[pd.isnull(df_pop.Province_State)]\n", "df_pop = df_pop.drop(\"UID\", axis=1)\n", "df_pop = df_pop.drop(\"iso2\", axis=1)\n", "df_pop = df_pop.drop(\"FIPS\", axis=1)\n", "df_pop = df_pop.drop(\"Admin2\", axis=1)\n", "df_pop = df_pop.drop(\"Province_State\", axis=1)\n", "df_pop = df_pop.drop(\"Combined_Key\", axis=1)\n", "df_pop = df_pop.drop(\"code3\", axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the dataset contains only population of 188 countries, and 5 columns instead of 12." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(188, 5)" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pop.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There were 2 empty cells in population column of the dataframe, they contained data on \"MS Zaandam\" and \"Diamond Princess\"." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "(186, 5)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pop = df_pop[df_pop.Country_Region !='MS Zaandam']\n", "df_pop = df_pop[df_pop.Country_Region !='Diamond Princess']\n", "df_pop.shape" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "df_pop = df_pop.reset_index(drop=True) #reset indexes for reduced number of rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If I check maximum population, I see population of China." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1404676330.0" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pop.Population.max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Minimum population is in Vatican (\"Holy See\")." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "809.0" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pop.Population.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vatican population is too small to be visible on the map or graph, so it is better to get rid of it in dataset." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(185, 5)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pop = df_pop[df_pop.Country_Region !='Holy See']\n", "df_pop.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now the dataset has the same number of countries, as the datasets of COVID-19 statistics. In order to use population data for calculation, I unify the \"iso3\"/\"ISO-3\" names of columns and merge \"Population\" column to df_deaths dataframe." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "df_pop = df_pop.rename(columns={\"iso3\": \"ISO-3\",\"Country_Region\": \"Country\"})" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryISO-3DateValueRegionIncomeLevelCountry_WB
0AfghanistanAFG1/22/200South AsiaLow incomeAfghanistan
\n", "
" ], "text/plain": [ " Country ISO-3 Date Value Region IncomeLevel Country_WB\n", "0 Afghanistan AFG 1/22/20 0 South Asia Low income Afghanistan" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_deaths.head(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both dataframes based on the same list of 185 countries." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted(df_pop['ISO-3'].unique().tolist()) == sorted(df_pop['ISO-3'].unique().tolist())" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "df_pop = df_pop.drop(axis=1, columns = ['Country', 'Lat', 'Long_'])" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "df_deaths_pop = df_deaths.merge(df_pop,on='ISO-3')" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryISO-3DateValueRegionIncomeLevelCountry_WBPopulationDeath_per_mlnDeath per mln
39371ZimbabweZWE8/18/20141Sub-Saharan AfricaLower middle incomeZimbabwe14862927.09.499.49
39372ZimbabweZWE8/19/20150Sub-Saharan AfricaLower middle incomeZimbabwe14862927.010.0910.09
39373ZimbabweZWE8/20/20151Sub-Saharan AfricaLower middle incomeZimbabwe14862927.010.1610.16
39374ZimbabweZWE8/21/20152Sub-Saharan AfricaLower middle incomeZimbabwe14862927.010.2310.23
39375ZimbabweZWE8/22/20153Sub-Saharan AfricaLower middle incomeZimbabwe14862927.010.2910.29
\n", "
" ], "text/plain": [ " Country ISO-3 Date Value Region \\\n", "39371 Zimbabwe ZWE 8/18/20 141 Sub-Saharan Africa \n", "39372 Zimbabwe ZWE 8/19/20 150 Sub-Saharan Africa \n", "39373 Zimbabwe ZWE 8/20/20 151 Sub-Saharan Africa \n", "39374 Zimbabwe ZWE 8/21/20 152 Sub-Saharan Africa \n", "39375 Zimbabwe ZWE 8/22/20 153 Sub-Saharan Africa \n", "\n", " IncomeLevel Country_WB Population Death_per_mln \\\n", "39371 Lower middle income Zimbabwe 14862927.0 9.49 \n", "39372 Lower middle income Zimbabwe 14862927.0 10.09 \n", "39373 Lower middle income Zimbabwe 14862927.0 10.16 \n", "39374 Lower middle income Zimbabwe 14862927.0 10.23 \n", "39375 Lower middle income Zimbabwe 14862927.0 10.29 \n", "\n", " Death per mln \n", "39371 9.49 \n", "39372 10.09 \n", "39373 10.16 \n", "39374 10.23 \n", "39375 10.29 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_deaths_pop[\"Death per mln\"] = round((df_deaths_pop.Value / df_deaths_pop.Population * 1000000),2)\n", "df_deaths_pop.tail()" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n", "1237.55\n", "40.3\n" ] } ], "source": [ "print(df_deaths_pop[\"Death per mln\"].min())\n", "print(df_deaths_pop[\"Death per mln\"].max())\n", "print(round(df_deaths_pop[\"Death per mln\"].mean(),2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §2.2. Animation of the map over time: deaths per million" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dataset 2: Oxford Covid-19 Government Response Tracker" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This part of the project is based on ideas \"Variation in governmentresponses to COVID-19\" paper by Oxford:\n", "\n", "\n", "\"As governments continue to respond to COVID-19, it is imperative to study what measures are effective and which are not. While the data presented here do, of course, not measure effectiveness directly, they can be useful input to studies that analyse factors affecting disease progression. OxCGRT seeks to contribute to this knowledge gap by providing comparable measures of individual policy actions, as well as several comparable aggregate indices. We find significant variation in both the measures that governments adopt and when they adopt them. Going forward, governments will benefit from adopting an evidence-based approach to the measures they deploy.\"\n", "\n", "Paper: https://www.bsg.ox.ac.uk/sites/default/files/2020-05/BSG-WP-2020-032-v6.0.pdf\n", "\n", "\n", "Data: https://github.com/OxCGRT/covid-policy-tracker" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Government response index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### §5.1. Cleaning and preparing for plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Oxford Covid-19 Government Response Tracker (OxCGRT) collects systematic information on which governments have taken which measures, and when. The data is daily published at project's GitHub page https://github.com/OxCGRT/covid-policy-tracker " ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(43660, 42)" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import requests #to access the csv from the url string\n", "import io #to read the csv directly from the url string\n", "import pandas as pd #to work with tabular data\n", "import pycountry #to get the three-letter country codes ISO 3166–1 for each country\n", "\n", "url_OxCGRT = \"https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/OxCGRT_latest.csv\"\n", "raw=requests.get(url_OxCGRT).content\n", "df_OxCGRT=pd.read_csv(io.StringIO(raw.decode('utf-8')))\n", "df_OxCGRT.shape" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Minimum date in YYYYMMDD format is 20200101\n", "Maximum date in YYYYMMDD format is 20200823\n" ] } ], "source": [ "print(\"Minimum date in YYYYMMDD format is\", df_OxCGRT.Date.min())\n", "print(\"Maximum date in YYYYMMDD format is\", df_OxCGRT.Date.max())" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryNameCountryCodeDateC1_School closingC1_FlagC2_Workplace closingC2_FlagC3_Cancel public eventsC3_FlagC4_Restrictions on gatherings...StringencyIndexStringencyIndexForDisplayStringencyLegacyIndexStringencyLegacyIndexForDisplayGovernmentResponseIndexGovernmentResponseIndexForDisplayContainmentHealthIndexContainmentHealthIndexForDisplayEconomicSupportIndexEconomicSupportIndexForDisplay
181ArubaABW202006300.0NaN1.01.00.0NaN0.0...32.4132.4141.6741.6744.8744.8737.1237.1287.587.5
417AfghanistanAFG202006303.01.03.00.02.01.04.0...78.7078.7076.1976.1960.9060.9071.9771.970.00.0
653AngolaAGO202006303.01.02.00.02.01.03.0...75.9375.9383.3383.3361.5461.5468.1868.1825.025.0
\n", "

3 rows × 42 columns

\n", "
" ], "text/plain": [ " CountryName CountryCode Date C1_School closing C1_Flag \\\n", "181 Aruba ABW 20200630 0.0 NaN \n", "417 Afghanistan AFG 20200630 3.0 1.0 \n", "653 Angola AGO 20200630 3.0 1.0 \n", "\n", " C2_Workplace closing C2_Flag C3_Cancel public events C3_Flag \\\n", "181 1.0 1.0 0.0 NaN \n", "417 3.0 0.0 2.0 1.0 \n", "653 2.0 0.0 2.0 1.0 \n", "\n", " C4_Restrictions on gatherings ... StringencyIndex \\\n", "181 0.0 ... 32.41 \n", "417 4.0 ... 78.70 \n", "653 3.0 ... 75.93 \n", "\n", " StringencyIndexForDisplay StringencyLegacyIndex \\\n", "181 32.41 41.67 \n", "417 78.70 76.19 \n", "653 75.93 83.33 \n", "\n", " StringencyLegacyIndexForDisplay GovernmentResponseIndex \\\n", "181 41.67 44.87 \n", "417 76.19 60.90 \n", "653 83.33 61.54 \n", "\n", " GovernmentResponseIndexForDisplay ContainmentHealthIndex \\\n", "181 44.87 37.12 \n", "417 60.90 71.97 \n", "653 61.54 68.18 \n", "\n", " ContainmentHealthIndexForDisplay EconomicSupportIndex \\\n", "181 37.12 87.5 \n", "417 71.97 0.0 \n", "653 68.18 25.0 \n", "\n", " EconomicSupportIndexForDisplay \n", "181 87.5 \n", "417 0.0 \n", "653 25.0 \n", "\n", "[3 rows x 42 columns]" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_OxCGRT[df_OxCGRT.Date == 20200630].head(3)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryNameCountryCodeDateConfirmedCasesConfirmedDeathsStringencyIndexForDisplayGovernmentResponseIndexForDisplayContainmentHealthIndexForDisplayEconomicSupportIndexForDisplay
232ArubaABW202008201296.05.047.2251.2849.2462.5
468AfghanistanAFG2020082037759.01383.062.0449.3658.330.0
704AngolaAGO202008201966.090.079.1767.6375.3825.0
940AlbaniaALB202008207812.0234.053.7055.1356.0650.0
1176AndorraAND202008201024.053.044.4458.9751.52100.0
..............................
42712KosovoRKS2020082011545.0390.069.4464.1064.3962.5
42948AnguillaAIA202008203.00.026.8536.5434.0950.0
43184Falkland IslandsFLK2020082013.00.027.7839.7437.88NaN
43420MontserratMSR20200820NaNNaN64.8165.3868.18NaN
43656Pitcairn IslandsPCN20200820NaNNaN11.11NaNNaNNaN
\n", "

185 rows × 9 columns

\n", "
" ], "text/plain": [ " CountryName CountryCode Date ConfirmedCases \\\n", "232 Aruba ABW 20200820 1296.0 \n", "468 Afghanistan AFG 20200820 37759.0 \n", "704 Angola AGO 20200820 1966.0 \n", "940 Albania ALB 20200820 7812.0 \n", "1176 Andorra AND 20200820 1024.0 \n", "... ... ... ... ... \n", "42712 Kosovo RKS 20200820 11545.0 \n", "42948 Anguilla AIA 20200820 3.0 \n", "43184 Falkland Islands FLK 20200820 13.0 \n", "43420 Montserrat MSR 20200820 NaN \n", "43656 Pitcairn Islands PCN 20200820 NaN \n", "\n", " ConfirmedDeaths StringencyIndexForDisplay \\\n", "232 5.0 47.22 \n", "468 1383.0 62.04 \n", "704 90.0 79.17 \n", "940 234.0 53.70 \n", "1176 53.0 44.44 \n", "... ... ... \n", "42712 390.0 69.44 \n", "42948 0.0 26.85 \n", "43184 0.0 27.78 \n", "43420 NaN 64.81 \n", "43656 NaN 11.11 \n", "\n", " GovernmentResponseIndexForDisplay ContainmentHealthIndexForDisplay \\\n", "232 51.28 49.24 \n", "468 49.36 58.33 \n", "704 67.63 75.38 \n", "940 55.13 56.06 \n", "1176 58.97 51.52 \n", "... ... ... \n", "42712 64.10 64.39 \n", "42948 36.54 34.09 \n", "43184 39.74 37.88 \n", "43420 65.38 68.18 \n", "43656 NaN NaN \n", "\n", " EconomicSupportIndexForDisplay \n", "232 62.5 \n", "468 0.0 \n", "704 25.0 \n", "940 50.0 \n", "1176 100.0 \n", "... ... \n", "42712 62.5 \n", "42948 50.0 \n", "43184 NaN \n", "43420 NaN \n", "43656 NaN \n", "\n", "[185 rows x 9 columns]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# drop excessive columns\n", "df_OxCGRT_indexes = df_OxCGRT.drop(axis=1, columns = ['C1_School closing', 'C1_Flag',\n", " 'C2_Workplace closing', 'C2_Flag',\n", " 'C3_Cancel public events', 'C3_Flag',\n", " 'C4_Restrictions on gatherings', 'C4_Flag',\n", " 'C5_Close public transport', 'C5_Flag',\n", " 'C6_Stay at home requirements', 'C6_Flag',\n", " 'C7_Restrictions on internal movement', 'C7_Flag',\n", " 'C8_International travel controls', \n", " 'E1_Income support', 'E1_Flag', \n", " 'E2_Debt/contract relief', 'E3_Fiscal measures', \n", " 'E4_International support', \n", " 'H1_Public information campaigns', 'H1_Flag',\n", " 'H2_Testing policy', 'H3_Contact tracing', \n", " 'H4_Emergency investment in healthcare', \n", " 'H5_Investment in vaccines', \n", " 'M1_Wildcard',\n", " 'StringencyIndex',\n", " 'StringencyLegacyIndex',\n", " 'StringencyLegacyIndexForDisplay',\n", " 'GovernmentResponseIndex',\n", " 'ContainmentHealthIndex',\n", " 'EconomicSupportIndex' \n", " ])\n", "df_OxCGRT_indexes[df_OxCGRT_indexes.Date == 20200820] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Oxford Covid-19 Government Response Tracker tracks individual policy measures across 17 indicators and calculate several indices to give an overall impression of government activity. \n", "\n", "I am particularly interested in 2 aggregated indexes calculated by Oxford:\n", "- GovernmentResponseIndexForDisplay (all 17 indicators)\n", "- EconomicSupportIndexForDisplay (2 indicators)\n", "\n", "Each of these indices report a number between 0 to 100 that reflects the level of the governments response along certain dimensions. This is a measure of how many of the relevant indicators a government has acted upon, and to what degree. The index cannot say whether a government's policy has been implemented effectively.\n", "\n", "Each index dataframe could be downloaded as separate .csv file from https://github.com/OxCGRT/covid-policy-tracker/tree/master/data/timeseries" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "172\n" ] } ], "source": [ "df_iGovResp=pd.read_csv(\"index_governmentresponse.csv\")\n", "print(len(df_iGovResp.dropna().CountryCode.tolist()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I will plot the data of United States, Canada, China, Brazil and Russia, and compare those countries' indexes to each other and world's average (calculated as mean of 172 countries available in this dataset). First, identify parts of dataframe I need to plot:" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "scrolled": true }, "outputs": [], "source": [ "ca_iGovResp = df_iGovResp[df_iGovResp.CountryName == 'Canada'].drop(axis=1, columns = ['CountryCode']) #Canada\n", "ru_iGovResp = df_iGovResp[df_iGovResp.CountryName == 'Russia'].drop(axis=1, columns = ['CountryCode']) #Russia \n", "wld_iGovResp = df_iGovResp.mean(axis=0, skipna=True).drop(axis=1, columns = ['CountryCode']) #\"World\", 172 countries \n", "cn_iGovResp = df_iGovResp[df_iGovResp.CountryName == 'China'].drop(axis=1, columns = ['CountryCode']) #China \n", "br_iGovResp = df_iGovResp[df_iGovResp.CountryName == 'Brazil'].drop(axis=1, columns = ['CountryCode']) #Brazil \n", "us_iGovResp = df_iGovResp[df_iGovResp.CountryName == 'United States'].drop(axis=1, columns = ['CountryCode']) #United States" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.style as style\n", "import numpy as np\n", "import seaborn as sns\n", "%matplotlib inline\n", "\n", "SMALL_SIZE = 12\n", "MEDIUM_SIZE = 14\n", "BIGGER_SIZE = 16\n", "\n", "plt.rc('font', size=MEDIUM_SIZE) # controls default text sizes\n", "plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title\n", "plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels\n", "plt.rc('xtick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels\n", "plt.rc('ytick', labelsize=MEDIUM_SIZE) # fontsize of the tick labels\n", "plt.rc('legend', fontsize=MEDIUM_SIZE) # legend fontsize\n", "plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from matplotlib.animation import FuncAnimation\n", "\n", "plt.figure(figsize=(15,8))\n", "plt.style.use('seaborn-whitegrid')\n", "\n", "plt.title('Government response index \\n January 1 - August 16')\n", "plt.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)\n", "plt.tick_params(axis = 'both', which = 'major')\n", "\n", "x = np.linspace(0, 228, 229) # x axis start point, end point and number of intervals\n", "xlim = (0, 229) # y axis start point, end point\n", "y_pos = np.arange(len(wld_iGovResp))\n", "\n", "\n", "# use the plt.xticks function to custom labels\n", "plt.xticks(y_pos, color='black', rotation=False)\n", "plt.xticks(np.arange(0, 230, 10.0))\n", "plt.yticks(np.arange(10, 110, 10))\n", "plt.xlabel('Days since January 1, 2020') \n", "plt.ylabel('Government response index (max = 100)')\n", "\n", "y1 = np.array(ca_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y2 = np.array(us_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y3 = np.array(cn_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y4 = np.array(br_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y5 = np.array(wld_iGovResp)\n", "y6 = np.array(ru_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "\n", "ca = plt.plot(x, y1, label='Canada',c=\"xkcd:leaf green\", lw=3, animated =True)\n", "us = plt.plot(x, y2, label='USA', c=\"royalblue\", lw=3, animated =True)\n", "cn = plt.plot(x, y3, label='China', c=\"mediumturquoise\", lw=3, animated =True) \n", "br = plt.plot(x, y4, label='Brazil', c=\"xkcd:pink\", lw=3, animated =True)\n", "wld = plt.plot(x, y5, label='World', ls='--',c='xkcd:dark grey', lw=3, animated =True)\n", "ru = plt.plot(x, y6, label='Russia', c=\"xkcd:tomato\", lw=3, animated =True)\n", "\n", "plt.axis([0, 229, 0, 100])\n", "plt.axhline(0, c='black', ls='-', lw=2)\n", "plt.axvline(0, c='black', ls='-', lw=2)\n", "plt.legend(loc='upper left', frameon=True, fancybox=True, framealpha=0.9, facecolor='white', ncol=3) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph shows government response index to COVID-19 for period since January 1st till August 16th.\n", "\n", "Government of China was the first one that responded to COVID-19, dealing with the outbreak first identified in Wuhan in December 2019. It took actions 50-70 days ahead of the rest of the world. Measures taken were more strict in terms of methods, to prevent more damage from virus. Both developing countries (like Brazil and Russia) and developed countries (United States and Canada) prefered not to make unpopular and costly decisions and not enforce strict measures to prevent virus spread till mid-March.\n", "\n", "Brazil government reaction was relatively mild in comparison with both all of the rest plotted countries and world's average. Russian government took more strict measures and keeps that level above world's average since March." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## §5.2. Aggregating data for correlation analysis" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(184, 10)\n", "(184, 10)\n" ] } ], "source": [ "data_all = df_deaths_pop #start aggregating data to single dataframe\n", "data_all = data_all[data_all.Date == '8/21/20'] #filter the latest day to research \n", "print(data_all.shape)\n", "data_all = data_all.rename(columns = {'Value':'Deaths',\n", " 'IncomeLevel':'Income', \n", " 'Death_per_mln':'Deaths per mln'})\n", "print(data_all.shape)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(184, 12)\n" ] } ], "source": [ "#merge Cases to data_all \n", "Cases = df_cases[df_cases['Date']=='8/21/20']\n", "Cases = Cases[Cases['Value']>0]\n", "Cases = Cases.drop(axis=1, columns = ['Country','Date','Region','IncomeLevel','Country_WB'])\n", "Cases = Cases.rename(columns = {'Value':'Cases'})\n", "data_all = data_all.merge(Cases, on = \"ISO-3\")\n", "\n", "#Calculate Cases per mln of population\n", "data_all['Cases per mln'] = data_all.Cases / data_all.Population *1000000\n", "print(data_all.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## §5.3. Correlation analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation analysis: Cases and Deaths " ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryISO-3DateDeathsRegionIncomeCountry_WBPopulationDeaths per mlnDeath per mlnCasesCases per mln
0AfghanistanAFG8/21/201385South AsiaLow incomeAfghanistan38928341.035.5835.5837894973.429615
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" ], "text/plain": [ " Country ISO-3 Date Deaths Region Income Country_WB \\\n", "0 Afghanistan AFG 8/21/20 1385 South Asia Low income Afghanistan \n", "\n", " Population Deaths per mln Death per mln Cases Cases per mln \n", "0 38928341.0 35.58 35.58 37894 973.429615 " ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_all.head(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I explore the correlation between columns." ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: Do dumulative cases correlate with cumulative deaths?\n", "A: Yes, correlation is positive and strong, standard correlation coefficient is 0.94\n" ] }, { "data": { "text/plain": [ "0.94" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Q: Do dumulative cases correlate with cumulative deaths?\") \n", "print(\"A: Yes, correlation is positive and strong, standard correlation coefficient is 0.94\")\n", "data_all_nonulls = data_all[data_all.Deaths != 0] #filter out 12 countries not reported deaths \n", "r = data_all_nonulls.Deaths.corr(data_all_nonulls.Cases)\n", "round(r,2)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ "Afghanistan" ], [ "Bangladesh" ], [ "India" ], [ "Maldives" ], [ "Nepal" ], [ "Pakistan" ], [ "Sri Lanka" ] ], "hovertemplate": "Region=South Asia
Cases=%{x}
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\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Cases vs Deaths plot \n", "import plotly.express as px\n", "df = data_all_nonulls\n", "fig = px.scatter(df, x=df['Cases'], y=df['Deaths'], \n", " color='Region', size=round(df['Cases per mln'],1), \n", " hover_data=['Country'],\n", " labels={'x':'Cases', 'y':'Deaths', 'size':'Cases per mln'}\n", " )\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: log(Deaths) correlate with log(Cases)?\n", "A: Yes, correlation is positive and very strong, r = 0.93\n" ] }, { "data": { "text/plain": [ "0.94" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Q: log(Deaths) correlate with log(Cases)?\")\n", "print(\"A: Yes, correlation is positive and very strong, r = 0.93\")\n", "r = (np.log(data_all_nonulls.Deaths).corr(np.log(data_all_nonulls.Cases), method = \"pearson\"))\n", "round(r,2)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ "Afghanistan" ], [ "Bangladesh" ], [ "India" ], [ "Maldives" ], [ "Nepal" ], [ "Pakistan" ], [ "Sri Lanka" ] ], "hovertemplate": "Region=South Asia
Confirmed cases (logarithmic axis)=%{x}
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"" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Confirmed cases (logarithmic axis)" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Deaths (logarithmic axis)" } } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# log(Cases) correlation with (Deaths) plot\n", "import plotly.express as px\n", "import plotly.io as pio\n", "pio.templates\n", "for template in [\"plotly_dark\"]:\n", " df = data_all_nonulls\n", " fig = px.scatter(df, \n", " x=np.log(df['Cases']), \n", " y=np.log(df['Deaths']), \n", " color='Region', \n", " size=df['Deaths per mln'], \n", " hover_data=['Country'],\n", " labels={'x':'Confirmed cases (logarithmic axis)', 'y':'Deaths (logarithmic axis)'},\n", "\n", " )\n", " fig.show()" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: Deaths correlate with Deaths per mln?\n", "A: Yes, but positive correlation is weak and insignificant, r = 0.44\n" ] }, { "data": { "text/plain": [ "0.44" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Q: Deaths correlate with Deaths per mln?\")\n", "print(\"A: Yes, but positive correlation is weak and insignificant, r = 0.44\")\n", "r = data_all_nonulls.Deaths.corr(data_all_nonulls['Deaths per mln'], method = \"pearson\") \n", "round(r,2)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: log(Deaths) correlate with log(Deaths per mln)?\n", "A: Yes, positive correlation is quite strong, r = 0.7\n" ] }, { "data": { "text/plain": [ "0.68" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Q: log(Deaths) correlate with log(Deaths per mln)?\")\n", "print(\"A: Yes, positive correlation is quite strong, r = 0.7\")\n", "r = np.log(data_all_nonulls.Deaths).corr(np.log(data_all_nonulls['Deaths per mln']),method = \"pearson\")\n", "round(r,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation analysis: Cases, Deaths and Response" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(185, 2)\n" ] } ], "source": [ "#Creating dataframe on maximum government response index\n", "resp = pd.DataFrame(df_iGovResp.max(axis=1))\n", "ind = pd.DataFrame(df_iGovResp.CountryCode)\n", "Response = (resp.merge(ind, \n", " left_index=True, \n", " right_index=True)).rename(columns={\"CountryCode\": \"ISO-3\", \n", " 0: \"Response\"})\n", "print(Response.shape)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(158, 13)\n" ] } ], "source": [ "# Merging maximum government response index into data_all, ignoring 0 deaths\n", "data_all_nonulls = data_all[data_all.Deaths != 0] #filter out 12 countries not reported deaths \n", "data_all_nonulls = data_all_nonulls.merge(Response, on = \"ISO-3\")\n", "print(data_all_nonulls.shape)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: Deaths correlates with maximum government response?\n", "A: No, r = 0.2\n" ] }, { "data": { "text/plain": [ "0.19" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Q: Deaths correlates with maximum government response?\")\n", "print(\"A: No, r = 0.2\")\n", "r = (np.log(data_all_nonulls.Deaths).corr(data_all_nonulls.Response, method = \"pearson\"))\n", "round(r,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Total deaths are not correlated to maximum government response, as many countries started to take measures before epidemic led to deaths number grouth in order to prevent the losses, and many of them keep high level of restrictions after the deaths number starts to decrease." ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: Deaths per mln correlate with maximum government response index?\n", "A: No, r = 0.1\n" ] }, { "data": { "text/plain": [ "0.12" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Q: Deaths per mln correlate with maximum government response index?\")\n", "print(\"A: No, r = 0.1\")\n", "round(data_all_nonulls['Deaths per mln'].corr(data_all_nonulls.Response),2) " ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: Cases correlate with maximum government response?\n", "A: No, r = 0.3\n" ] }, { "data": { "text/plain": [ "0.26" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"Q: Cases correlate with maximum government response?\")\n", "print(\"A: No, r = 0.3\")\n", "r = (np.log(data_all_nonulls['Cases']).corr(data_all_nonulls.Response, method = \"pearson\"))\n", "round(r,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation analysis: government response and government health expenditure" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [], "source": [ "# Merging Response and Government health expenditure per capita 2017, international $ \n", "import world_bank_data as wb\n", "df_govHealth = pd.DataFrame(wb.get_series('SH.XPD.GHED.PP.CD',date='2017',id_or_value=\"id\",simplify_index=True).dropna())\n", "df_govHealth['ISO-3'] = df_govHealth.index\n", "Response_Funding = Response.merge(df_govHealth, on = 'ISO-3')" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Q: Government response index correlates to government health expenditure?\n", "A: No, there is no correlation (corr coeff = 0.06)\n", "0.06\n" ] } ], "source": [ "print(\"Q: Government response index correlates to government health expenditure?\")\n", "print(\"A: No, there is no correlation (corr coeff = 0.06)\")\n", "print(round(Response_Funding['Response'].corr(Response_Funding['SH.XPD.GHED.PP.CD']),2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot: government response index by country over time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Strong correlation between number of confirmed cases and government response index over time is easy to see at the next plot.\n" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from matplotlib.animation import FuncAnimation\n", "\n", "plt.figure(figsize=(12,6))\n", "plt.style.use('seaborn-whitegrid')\n", "\n", "plt.title('Government response index \\n January 1 - August 16')\n", "plt.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)\n", "plt.tick_params(axis = 'both', which = 'major')\n", "\n", "x = np.linspace(0, 228, 229) # x axis start point, end point and number of intervals\n", "xlim = (0, 229) # y axis start point, end point\n", "y_pos = np.arange(len(wld_iGovResp))\n", "\n", "# use the plt.xticks function to custom labels\n", "plt.xticks(y_pos, color='black', rotation=False)\n", "plt.xticks(np.arange(0, 230, 10.0))\n", "plt.yticks(np.arange(10, 110, 10))\n", "plt.xlabel('Days since January 1, 2020') \n", "plt.ylabel('Government response index (max = 100)')\n", "\n", "y1 = np.array(ca_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y2 = np.array(us_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y3 = np.array(cn_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y4 = np.array(br_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "y5 = np.array(wld_iGovResp)\n", "y6 = np.array(ru_iGovResp.drop(axis=1, columns = 'CountryName').values.tolist()[0])\n", "\n", "ca = plt.plot(x, y1, label='Canada',c=\"xkcd:leaf green\", lw=3, animated =True)\n", "us = plt.plot(x, y2, label='USA', c=\"royalblue\", lw=3, animated =True)\n", "cn = plt.plot(x, y3, label='China', c=\"mediumturquoise\", lw=3, animated =True) \n", "br = plt.plot(x, y4, label='Brazil', c=\"xkcd:pink\", lw=3, animated =True)\n", "wld = plt.plot(x, y5, label='World', ls='--',c='xkcd:dark grey', lw=3, animated =True)\n", "ru = plt.plot(x, y6, label='Russia', c=\"xkcd:tomato\", lw=3, animated =True)\n", "\n", "plt.axis([0, 229, 0, 100])\n", "plt.axhline(0, c='black', ls='-', lw=2)\n", "plt.axvline(0, c='black', ls='-', lw=2)\n", "plt.legend(loc='best', frameon=True, fancybox=True, framealpha=0.9, facecolor='white', ncol=2) \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correlation analysis: confirmed cases and government response index over time" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [], "source": [ "# log(Cases) correlation with Response over time\n", "#RESPONSE Jan 22-Aug 16\n", "y1[21:] #ca_iGovResp Jan 22-Aug 16\n", "y2[21:] #us_iGovResp Jan 22-Aug 16\n", "y3[21:] #cn_iGovResp Jan 22-Aug 16\n", "y4[21:] #br_iGovResp Jan 22-Aug 16\n", "#y5[21:] #wld_iGovResp Jan 22-Aug 16\n", "y6[21:] #ru_iGovResp Jan 22-Aug 16\n", "#print(len(y1[21:]))\n", "\n", "#CASES Jan 22-Aug 16\n", "cases_part = pd.read_csv(\"df_cases.csv\")\n", "#print(len(cases_part.Date.unique()))\n", "x1 = np.array(cases_part.Value[cases_part['ISO-3']=='CAN'])\n", "x2 = np.array(cases_part.Value[cases_part['ISO-3']=='USA'])\n", "x3 = np.array(cases_part.Value[cases_part['ISO-3']=='CHN'])\n", "x4 = np.array(cases_part.Value[cases_part['ISO-3']=='BRA'])\n", "#x5 = np.array(cases_part.groupby('Date')['Value'].mean()) #world's average, !sorted\n", "x6 = np.array(cases_part.Value[cases_part['ISO-3']=='RUS'])" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "Canada = pd.DataFrame(x1,y1[21:])\n", "Canada['Response'] = Canada.index\n", "Canada['Country'] = \"Canada\"\n", "Canada = Canada.rename(columns={0: \"Cases\"}).reset_index(drop=True)\n", "\n", "USA = pd.DataFrame(x2,y2[21:])\n", "USA['Response'] = USA.index\n", "USA['Country'] = \"USA\"\n", "USA = USA.rename(columns={0: \"Cases\"}).reset_index(drop=True)\n", "\n", "China = pd.DataFrame(x3,y3[21:])\n", "China['Response'] = China.index\n", "China['Country'] = \"China\"\n", "China = China.rename(columns={0: \"Cases\"}).reset_index(drop=True)\n", "\n", "Brazil = pd.DataFrame(x4,y4[21:])\n", "Brazil['Response'] = Brazil.index\n", "Brazil['Country'] = \"Brazil\"\n", "Brazil = Brazil.rename(columns={0: \"Cases\"}).reset_index(drop=True)\n", "\n", "Russia = pd.DataFrame(x6,y6[21:])\n", "Russia['Response'] = Russia.index\n", "Russia['Country'] = 'Russia'\n", "Russia = Russia.rename(columns={0: \"Cases\"}).reset_index(drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To find HEX codes for this colors (Canada 'leaf green', USA 'royalblue', China 'mediumturquoise\", Brazil 'pink', Russia 'tomato'), view the list of colors from mathplotlib:" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "import matplotlib\n", "colorname = []\n", "colorid = []\n", "\n", "for name, hex in matplotlib.colors.cnames.items():\n", " colorname.append(name)\n", " colorid.append(hex)\n", "zippedcolors = list(zip(colorname, colorid))\n", "zippedcolors = sorted(zippedcolors, key=lambda x: x[1])\n", 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cases and response" }, "width": 1000, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "type": "log" }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ] } } }, "text/html": [ "
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\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# log(Cases) correlation with (Government response index) over time\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "import plotly.graph_objects as go\n", "\n", "fig = make_subplots(rows=1, cols=1)\n", "\n", "fig.append_trace(go.Scatter(x = Canada['Cases'], \n", " y = Canada['Response'], name=\"Canada\",\n", " mode=\"lines\", line=dict(color='#5ca904')), \n", " row=1, col=1)\n", "\n", "fig.append_trace(go.Scatter(x = USA['Cases'], \n", " y = USA['Response'], name=\"USA\",\n", " mode=\"lines\", line=dict(color='#4169E1')),\n", " row=1, col=1)\n", "\n", "fig.append_trace(go.Scatter(x = China['Cases'], \n", " y = China['Response'], name=\"China\",\n", " mode=\"lines\", line=dict(color='#48D1CC')),\n", " row=1, col=1)\n", "\n", "fig.append_trace(go.Scatter(x = Brazil['Cases'], \n", " y = Brazil['Response'], name=\"Brazil\",\n", " mode=\"lines\", line=dict(color='#FFC0CB')),\n", " row=1, col=1)\n", "\n", "fig.append_trace(go.Scatter(x = Russia['Cases'], \n", " y = Russia['Response'], name=\"Russia\",\n", " mode=\"lines\", line=dict(color='#FF6347')),\n", " row=1, col=1)\n", "\n", "fig.layout \n", "fig.update_layout(xaxis_type=\"log\",height=600, width=1000, \n", " title_text=\"Correlation of cases and response\",\n", " showlegend=True,\n", " legend=dict(yanchor=\"top\", \n", " y=0.99, \n", " xanchor=\"left\",x=0.01),\n", " legend_title_text='Country'\n", " )\n", "fig.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Research questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- is there a significant correlation between cumulative deaths from COVID-19 and maximum government response index?\n", "\n", "\n", "- is there a significant correlation between government response index and government funding healthcare in previous years?\n", "\n", "\n", "- is there strong positive correlation between confirmed COVID-19 cases and government response index over time?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Research results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- No, there is no significant correlation between deaths from COVID-19 and government response to COVID. Probably because such government measures are being globally implemented in order to prevent further virus spread and it's damage. \n", "\n", "\n", "- No, there is no correlation between government response index and government funding healthcare in previous years. The governments that show higher response index to COVID-19 in 2020 were not necesseraly better in funding their healthcare systems in previous years.\n", "\n", "\n", "- Correlation between confirmed cases and government response over time is very strong (coefficient is equal to 0.94). But correlation between total cumulative number of cases and maximum government response response is very weak (only 0.26), and correlation between deaths and maximum government response is (0.19). It means, that time factor is crucial to government response in order for measures taken have positive effect on the situation with virus spread.\n", "\n", "\n", "In any case, it is too early to make final conclusions as desease statistics database is only growing." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }